{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8bb7565e-8695-4f91-a070-3348a1e99e7d",
   "metadata": {},
   "source": [
    "# 箱型图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0410ed-bc44-4157-809f-1cd1d9fce985",
   "metadata": {},
   "source": [
    "`boxplot`方法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c2910cb-aca2-4731-a7c1-7fbe27e61723",
   "metadata": {},
   "source": [
    "- x：这是主要的数据输入，可以是一维数组或列表，也可以是二维数组，其中每一列代表一组数据。\n",
    "- notch：如果设置为 True，则绘制凹口箱形图，这有助于比较不同组的中位数。\n",
    "- sym：指定异常值的标记样式，默认是 '+'，可以是其他任何 matplotlib 支持的标记。\n",
    "- vert：默认为 True，表示箱形图是垂直的；如果设置为 False，则箱形图水平显示。\n",
    "- whis：定义了从上下四分位数延伸出去的线的最大长度，通常是以 IQR（四分位距）的倍数来定义。\n",
    "- bootstrap：当 notch=True 时，这个参数决定了凹口的置信区间计算方法。\n",
    "- patch_artist：如果设置为 True，那么每个箱子将被填充颜色，可以通过 set_facecolor 方法来改变颜色。\n",
    "- meanline 和 showmeans：这两个参数可以用来显示平均值线或点。\n",
    "- showcaps、showfliers、showbox 和 showcaps：这些参数控制是否显示箱形图的各个部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "006a9e48-6b51-4077-815b-a433f68d0c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2219d53e-5d19-4e2e-aa3e-22779ad04055",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.normal(size=(500, 4))\n",
    "labels = list('ABCD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "561f87a5-ed69-4328-a6b2-d274472aca36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x=data, tick_labels=labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97a3cd52-6d7a-4619-a3ad-5f6f7448ace8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
